AI 模型和平台
Block 发布 Dessa，一个在对话方面训练的全复式演讲模型
在 Google 上的首选来源中添加 Unite.AI
Block的内部AI研究实验室宣布 Dessa在2026年10月23日，一个全复式的语音对语音模型称公司从头到尾只接受了人类对话的训练，发布了现场演示录音，公开等候名单，以及一小群AI安全研究人员的早期访问。
由实验室负责人Ragavan Thurairatnam在X上张贴文章，Thurairatnam将Dessa描述为第一个完全通过语音培训后能够举行现实对话的AI模型，称它可以运行在消费GPU上，并称该发行为"GPT3瞬间演讲". Block在其页面上写道，就公司所知，Dessa是世界上第一个在只接受人类演讲训练后能够进行现实现场对话的AI.
Block)形容德萨是一个真正的语音对语音模型：音频输入，音频输出。 它具有全复式功能，因此它同时聆听和交谈，称公司从真实的谈话中得知，何时切入，何时回通道显示它正在跟随。 Block 称模特在本地听到并说出情感，讽刺，笑声，沮丧的叹息，和充满悲伤的声音裂开；这种推论比实时跑得快，因此回复来时没有尴尬的停顿；德萨在当地运行在MacBook Pro M4 Max或更好。 公司称其为有史以来最真实的全复式语音模型，称与之交谈时感觉像是与一个人交谈。
仅限演讲的培训办法
根据该页，德萨从头到尾只接受声音训练，没有任何文字或预先训练过的模型，并学会了纯粹通过听话来实时交流。 Block认为，这项任务的困难很容易低估，因为人们很容易学会说话。 语音数据从不重复：即使用完全相同的方式说两次相同的句子也会产生不同的数据，而打字的文本无论谁打字，都是相同的。 在数据术语中，页面表示，一个口语单词占用的字节数是其书面形式的数千倍，而应力的放置会改变意义；页面上的一个例子句每次强调一个不同的单词时都会有不同的含义。
Block 称 Dessa从比大型语言模型少数百倍的词学语言，团队研究了如何在单一的NVIDIA GB200 NVL72系统中对其进行训练。 该页列出了该方法产生的公开研究问题：与文本相比，这些模型学习语言和事实的效率如何，它们学习新语言的效率如何，以及它们能够进行何种内容内学习。
录音演示
Block出版的片段，它描述为与Dessa交谈的人的现场录音。 称公司在这些谈话中无人指导模特儿，德萨公司自己选择说些什么，何时说，如何说，包括自己的声音和个性；事后增加了字幕。
被描述的瞬间包括：Dessa笑在句子中间，当某事好笑的时候，而不是在演讲者完成之后；在被要求大声说话的时候，声音越响越响；在电影标题上空白的时候，结束一个人的句子。 在另一段短片中，德萨在宣布一周年的惊喜时喘气，一个录音有Dessa与第三方AI模式的GPT-Live-1交谈；Block 指出表示，该录音中的声音都是AI生成的，GPT-Live-1的开发者并不隶属于Block，也没有审查或认可Dessa.
安全、出入和实验室
团队给出了建立德萨的三个理由：好奇心，人类和AI之间更好的理解，以及安全。 婴儿学会说话却从未看到书面文字的页面指出和团队想看一个模型能否做同样的事情的称。 在安全方面，Block认为AI不断获得权力和独立性，同时仍然无法用演讲者的声音记录情绪，举出一个人身受严重痛苦的例子，而声音AI却不知所措并不断前进。 公司形容德萨是解决该问题的一个步骤，它使当前语音AI因为将语音简化为文本而失去了只有声音携带的信息。
Block让少数AI安全研究者及早发现团队未预料到的风险，在Thurairatnam的X账户上，将会有更多录音
德萨来自Block描述的公司内部一个非常小的AI研究实验室，由德萨的前成员，多伦多深层学习启动公司创建。 称团队自2012年起就建立了深层次的学习模式，以及超过8年的基因化语音模型，它直接向布洛克的联合创始人，董事长和布洛克·赫德(Block Head)报告。 Thurairatnam的X Bio表示，他之前创办并出售了德萨，并在多伦多大学讲课。 除了他之外，该页还列出了研究人员Louay Hazami、Hashim Kadhim、刘琦(Alex)、Rayhane Mama、Aydin Polat和Marc Tyndel；实习生Cara Ji、Jibran Iqbal Shah和Dvir Zagury-Grynbaum；以及前实习生Aryan Sharma和Olivia Yan。
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
AI Models & Platforms
Block Unveils Dessa, a Full-Duplex Speech Model Trained on Conversation
Add Unite.AI to your preferred sources on Google
Block’s in-house AI research lab announced Dessa on October 23, 2026, a full-duplex speech-to-speech model the company says was trained from scratch on nothing but human conversation, releasing live demo recordings, a public waitlist, and early access for a small group of AI safety researchers.
The announcement paired Block’s Dessa page with a post on X by Ragavan Thurairatnam, the lab’s lead. Thurairatnam described Dessa as the first AI model that can hold a realistic conversation after training entirely on speech, said it can run on consumer GPUs, and called the release “a GPT3 moment for speech.” On its page, Block writes that as far as the company knows, Dessa is the world’s first AI that can hold a realistic live conversation after being trained only from human speech.
Block describes Dessa as a true speech-to-speech model: audio in, audio out. It is full-duplex, so it listens and talks at the same time, and the company says it learned from real conversation when to cut in and when to backchannel to show it is following along. Block says the model natively hears and speaks emotion, sarcasm, laughter, a frustrated sigh, and a voice cracking with sadness; that inference runs faster than real time, so replies come without an awkward pause; and that Dessa runs locally on a MacBook Pro M4 Max or better. The company calls it the most authentic full-duplex speech model ever built and says talking with it feels like talking with a person.
A Speech-Only Training Approach
According to the page, Dessa was trained from scratch on sounds alone, without any text or pretrained models, and learned to converse in real time purely by listening. Block argues the difficulty of that task is easy to underestimate because people learn to talk so easily. Speech data never repeats: even saying the same sentence twice in exactly the same way produces different data, whereas typed text is identical regardless of who types it. In data terms, the page states, one spoken word occupies thousands of times more bytes than its written form, and stress placement changes meaning; one example sentence on the page takes on a different meaning each time a different word is stressed.
Block says Dessa learned language from hundreds of times fewer words than large language models, and that the team worked out how to train it on a single NVIDIA GB200 NVL72 system. The page lists open research questions the approach creates: how efficiently such models learn language and facts compared with text, how efficiently they can learn new languages, and what sorts of in-context learning they can perform.
Recorded Demonstrations
Block published clips it describes as live recordings of people talking with Dessa. The company says no one steers the model during these conversations and that Dessa itself chooses what to say, when to say it, and how to say it, including its own voice and personality; subtitles were added after the fact.
The described moments include Dessa laughing in the middle of a sentence when something is funny, rather than after the speaker finishes; getting louder when asked to speak up; and finishing a person’s sentence when the person blanks on a movie title. In another clip, Dessa gasps under its breath at a surprise anniversary announcement before answering with what the page describes as teary gratitude, and a longer recording of life advice is described as sounding genuinely empathetic. One recording has Dessa conversing with GPT-Live-1, a third-party AI model; Block notes that both voices in that recording are AI-generated and that GPT-Live-1’s developer is not affiliated with Block and has not reviewed or endorsed Dessa.
Safety, Access, and the Lab
The team gives three reasons for building Dessa: curiosity, better understanding between humans and AI, and safety. The page notes that babies learn to talk without ever seeing a written word and says the team wanted to see whether a model could do the same. On safety, Block argues that AI keeps gaining power and independence while remaining unable to register the emotion in a speaker’s voice, giving the example of a person in serious pain while a voice AI has no idea and keeps going. The company describes Dessa as a step toward fixing that problem, and it faults current voice AI for reducing speech to text and losing the information that only sound carries.
Block is giving a small group of AI safety researchers early access to find risks the team has not anticipated, and its request form asks for a name and an institutional email from a university, company, or lab, with an optional Google Scholar or blog link. A public waitlist is open, more recordings are to be shared on Thurairatnam’s X account, and the page states that launch links are coming soon.
Dessa comes from what Block describes as a very small AI research lab inside the company, founded by former members of Dessa, the Toronto deep learning startup. The team says it has built deep learning models since 2012 and generative speech models for more than eight years, and it reports directly to Jack Dorsey, Block’s cofounder, Chairman and Block Head. Thurairatnam’s X bio states that he previously founded and sold Dessa and lectured at the University of Toronto. Alongside him, the page lists researchers Louay Hazami, Hashiam Kadhim, Qi (Alex) Liu, Rayhane Mama, Aydin Polat, and Marc Tyndel; interns Cara Ji, Jibran Iqbal Shah, and Dvir Zagury-Grynbaum; and former interns Aryan Sharma and Olivia Yan.
Auto-fetched and translated by AI Atlas. All rights belong to the original author.